Pp. Raghu et al., UNSUPERVISED TEXTURE CLASSIFICATION USING VECTOR QUANTIZATION AND DETERMINISTIC RELAXATION NEURAL-NETWORK, IEEE transactions on image processing, 6(10), 1997, pp. 1376-1387
Citations number
28
Categorie Soggetti
Computer Sciences, Special Topics","Engineering, Eletrical & Electronic","Computer Science Software Graphycs Programming","Computer Science Theory & Methods
This paper describes the use bf a neural network architecture for clas
sifying textured images in an unsupervised manner using image-specific
constraints, The texture features are extracted by using two-dimensio
nal (2-D) Gabor filters arranged as a set of wavelet bases, The classi
fication model comprises feature quantization, partition, and competit
ion processes, The feature quantization process uses a vector Quantize
r to quantize the features into codevectors, where the probability of
grouping the vectors is modeled as Gibbs distribution, A set of label
constraints for each pixel in the image are provided by the partition
and competition processes. An energy function corresponding to the a p
osteriori probability is derived from these processes, and a neural ne
twork is used to represent this energy function, The state of the netw
ork and the codevectors of tbe vector quantizer are iteratively adjust
ed using a deterministic relaxation procedure until a stable state Is
reached, The final equilibrium state of the vector quantizer gives a c
lassification of the textured image. A cluster validity measure based
on modified Hubert index is used to determine the optimal number of te
xture classes in the image.